results/orthofinder/output/Orthogroup_Sequences/OG0000000.fa
results/orthofinder/output/Orthogroup_Sequences/OG0000001.fa
results/orthofinder/output/Orthogroup_Sequences/OG0000002.fa
results/orthofinder/output/Orthogroup_Sequences/OG0000003.fa
results/orthofinder/output/Orthogroup_Sequences/OG0000004.fa
results/orthofinder/output/Orthogroup_Sequences/OG0000006.fa

results/orthofinder/orthosnap/OG0000005/OG0000005_orthosnap_0.fa

results/alignment/trimmed_protein/OG0000004.trimmed.protein.alignment.fa
results/alignment/trimmed_protein/OG0000000.trimmed.protein.alignment.fa

General Characteristics
=======================
6	Number of taxa
796	Alignment length
100	Parsimony informative sites
100	Variable sites
685	Constant sites

Character Frequencies
=====================
Y	176
W	52
V	338
T	242
S	200
R	217
Q	230
P	268
N	222
M	77
L	412
K	301
I	309
H	97
G	410
F	230
E	271
D	231
C	79
A	366
-	48

IQ-TREE 2.2.0.3 COVID-edition built Sep  5 2022

Input file name: results/supermatrix/supermatrix.protein.fa
Type of analysis: ModelFinder + tree reconstruction + ultrafast bootstrap (1000 replicates)
Random seed number: 554947

REFERENCES
----------

To cite IQ-TREE please use:

Bui Quang Minh, Heiko A. Schmidt, Olga Chernomor, Dominik Schrempf,
Michael D. Woodhams, Arndt von Haeseler, and Robert Lanfear (2020)
IQ-TREE 2: New models and efficient methods for phylogenetic inference
in the genomic era. Mol. Biol. Evol., in press.
https://doi.org/10.1093/molbev/msaa015

To cite ModelFinder please use: 

Subha Kalyaanamoorthy, Bui Quang Minh, Thomas KF Wong, Arndt von Haeseler,
and Lars S Jermiin (2017) ModelFinder: Fast model selection for
accurate phylogenetic estimates. Nature Methods, 14:587–589.
https://doi.org/10.1038/nmeth.4285

Since you used ultrafast bootstrap (UFBoot) please also cite: 

Diep Thi Hoang, Olga Chernomor, Arndt von Haeseler, Bui Quang Minh,
and Le Sy Vinh (2018) UFBoot2: Improving the ultrafast bootstrap
approximation. Mol. Biol. Evol., 35:518–522.
https://doi.org/10.1093/molbev/msx281

SEQUENCE ALIGNMENT
------------------

Input data: 6 sequences with 796 amino-acid sites
Number of constant sites: 553 (= 69.4724% of all sites)
Number of invariant (constant or ambiguous constant) sites: 553 (= 69.4724% of all sites)
Number of parsimony informative sites: 100
Number of distinct site patterns: 255

ModelFinder
-----------

Best-fit model according to BIC: cpREV+I+G4

List of models sorted by BIC scores: 

Model                  LogL         AIC      w-AIC        AICc     w-AICc         BIC      w-BIC
cpREV+I+G4        -4754.045    9530.090 +    0.864    9530.427 +    0.904    9581.566 +    0.563
cpREV+G4          -4757.639    9535.278 +   0.0645    9535.558 +   0.0695    9582.074 +    0.437
LG+I+G4           -4762.923    9547.846 -  0.00012    9548.183 - 0.000126    9599.322 - 7.85e-05
LG+G4             -4769.787    9559.575 - 3.42e-07    9559.855 - 3.68e-07    9606.371 - 2.31e-06
Q.pfam+I+G4       -4772.957    9567.914 - 5.28e-09    9568.250 - 5.53e-09    9619.389 - 3.45e-09
Q.pfam+G4         -4779.840    9579.680 - 1.47e-11    9579.960 - 1.59e-11    9626.476 - 9.96e-11
WAG+I+G4          -4778.199    9578.397 -  2.8e-11    9578.734 - 2.93e-11    9629.873 - 1.82e-11
WAG+G4            -4782.001    9584.002 -  1.7e-12    9584.283 - 1.83e-12    9630.798 - 1.15e-11
VT+G4             -4783.028    9586.055 - 6.08e-13    9586.335 - 6.54e-13    9632.851 - 4.11e-12
VT+I+G4           -4779.908    9581.816 - 5.06e-12    9582.153 -  5.3e-12    9633.291 -  3.3e-12
LG+I              -4785.119    9590.237 - 7.51e-14    9590.517 - 8.08e-14    9637.033 - 5.08e-13
Q.plant+I+G4      -4784.005    9590.009 - 8.41e-14    9590.346 - 8.81e-14    9641.485 - 5.49e-14
Q.yeast+I+G4      -4784.243    9590.486 - 6.63e-14    9590.823 - 6.94e-14    9641.962 - 4.32e-14
rtREV+I+G4        -4785.918    9593.835 - 1.24e-14    9594.172 -  1.3e-14    9645.311 -  8.1e-15
rtREV+G4          -4791.371    9602.742 - 1.45e-16    9603.022 - 1.56e-16    9649.538 - 9.79e-16
JTTDCMut+I+G4     -4789.487    9600.973 -  3.5e-16    9601.310 - 3.66e-16    9652.449 - 2.28e-16
Q.yeast+G4        -4793.400    9606.800 -  1.9e-17    9607.080 - 2.05e-17    9653.596 - 1.29e-16
Q.plant+G4        -4794.205    9608.410 -  8.5e-18    9608.691 - 9.15e-18    9655.206 - 5.75e-17
JTT+I+G4          -4791.124    9604.249 - 6.81e-17    9604.585 - 7.12e-17    9655.724 - 4.44e-17
JTTDCMut+G4       -4795.768    9611.536 - 1.78e-18    9611.816 - 1.92e-18    9658.332 -  1.2e-17
JTT+G4            -4797.364    9614.729 - 3.61e-19    9615.009 - 3.88e-19    9661.525 - 2.44e-18
Q.insect+I+G4     -4795.237    9612.475 - 1.11e-18    9612.811 - 1.17e-18    9663.950 - 7.26e-19
Blosum62+G4       -4799.131    9618.261 - 6.17e-20    9618.542 - 6.64e-20    9665.057 - 4.17e-19
Blosum62+I+G4     -4796.305    9614.610 - 3.83e-19    9614.947 - 4.01e-19    9666.086 -  2.5e-19
PMB+G4            -4804.054    9628.108 - 4.49e-22    9628.388 - 4.83e-22    9674.904 - 3.04e-21
Q.insect+G4       -4804.135    9628.271 - 4.14e-22    9628.551 - 4.45e-22    9675.067 -  2.8e-21
cpREV+F+I+G4      -4737.617    9535.233 +    0.066    9537.665 -   0.0242    9675.621 - 2.12e-21
cpREV+F+G4        -4741.076    9540.152 -  0.00564    9542.424 -  0.00224    9675.861 - 1.88e-21
PMB+I+G4          -4801.637    9625.275 - 1.85e-21    9625.612 - 1.94e-21    9676.750 - 1.21e-21
mtART+F+I+G4      -4744.357    9548.714 -  7.8e-05    9551.145 - 2.87e-05    9689.102 - 2.51e-24
mtZOA+F+I+G4      -4745.935    9551.870 - 1.61e-05    9554.301 - 5.91e-06    9692.258 - 5.18e-25
LG+F+I+G4         -4746.272    9552.545 - 1.15e-05    9554.976 - 4.22e-06    9692.933 - 3.69e-25
Dayhoff+I+G4      -4810.213    9642.426 - 3.49e-25    9642.763 - 3.65e-25    9693.902 - 2.28e-25
DCMut+I+G4        -4810.333    9642.665 -  3.1e-25    9643.002 - 3.24e-25    9694.141 - 2.02e-25
Dayhoff+G4        -4815.703    9651.406 - 3.92e-27    9651.687 - 4.22e-27    9698.202 - 2.65e-26
DCMut+G4          -4815.774    9651.549 - 3.65e-27    9651.829 - 3.93e-27    9698.345 - 2.47e-26
mtART+F+G4        -4752.459    9562.918 - 6.43e-08    9565.189 - 2.56e-08    9698.626 - 2.14e-26
LG+F+G4           -4752.616    9563.233 - 5.49e-08    9565.505 - 2.18e-08    9698.941 - 1.83e-26
mtZOA+F+G4        -4754.209    9566.418 - 1.12e-08    9568.689 - 4.44e-09    9702.126 - 3.73e-27
Q.pfam+F+I+G4     -4752.279    9564.559 - 2.83e-08    9566.990 - 1.04e-08    9704.947 - 9.09e-28
mtInv+F+I+G4      -4753.769    9567.537 - 6.38e-09    9569.969 - 2.34e-09    9707.925 - 2.05e-28
WAG+F+I+G4        -4753.892    9567.784 - 5.64e-09    9570.216 - 2.07e-09    9708.172 - 1.81e-28
WAG+F+G4          -4757.613    9573.227 - 3.71e-10    9575.498 - 1.48e-10    9708.935 - 1.24e-28
Q.pfam+F+G4       -4758.639    9575.278 - 1.33e-10    9577.550 - 5.29e-11    9710.987 - 4.44e-29
rtREV+F+I+G4      -4755.824    9571.649 - 8.16e-10    9574.080 -    3e-10    9712.037 - 2.62e-29
mtInv+F+G4        -4760.247    9578.495 - 2.66e-11    9580.766 - 1.06e-11    9714.203 - 8.89e-30
rtREV+F+G4        -4761.112    9580.224 - 1.12e-11    9582.496 - 4.46e-12    9715.933 - 3.74e-30
Q.yeast+F+I+G4    -4760.383    9580.766 - 8.55e-12    9583.198 - 3.14e-12    9721.154 - 2.75e-31
Q.plant+F+I+G4    -4762.682    9585.365 - 8.58e-13    9587.796 - 3.15e-13    9725.752 - 2.76e-32
mtREV+F+I+G4      -4763.806    9587.612 - 2.79e-13    9590.043 - 1.02e-13    9728.000 - 8.97e-33
Q.yeast+F+G4      -4768.290    9594.579 - 8.56e-15    9596.851 - 3.41e-15    9730.288 - 2.86e-33
mtREV+F+G4        -4770.353    9598.705 - 1.09e-15    9600.977 - 4.33e-16    9734.414 - 3.63e-34
VT+F+G4           -4771.629    9601.259 - 3.04e-16    9603.530 - 1.21e-16    9736.967 - 1.01e-34
Q.plant+F+G4      -4771.916    9601.831 - 2.28e-16    9604.103 - 9.07e-17    9737.540 - 7.61e-35
VT+F+I+G4         -4768.882    9597.764 - 1.74e-15    9600.196 -  6.4e-16    9738.152 -  5.6e-35
Q.insect+F+I+G4   -4768.958    9597.915 - 1.62e-15    9600.347 - 5.93e-16    9738.303 - 5.19e-35
Dayhoff+F+I+G4    -4769.817    9599.634 - 6.84e-16    9602.066 - 2.51e-16    9740.022 -  2.2e-35
DCMut+F+I+G4      -4769.947    9599.894 - 6.01e-16    9602.325 - 2.21e-16    9740.282 - 1.93e-35
JTTDCMut+F+I+G4   -4771.470    9602.940 - 1.31e-16    9605.371 - 4.81e-17    9743.328 - 4.21e-36
Dayhoff+F+G4      -4775.697    9609.395 - 5.19e-18    9611.666 - 2.07e-18    9745.103 - 1.73e-36
DCMut+F+G4        -4775.776    9609.552 -  4.8e-18    9611.823 - 1.91e-18    9745.260 -  1.6e-36
JTT+F+I+G4        -4773.051    9606.102 - 2.69e-17    9608.534 -  9.9e-18    9746.490 - 8.66e-37
Q.insect+F+G4     -4776.671    9611.342 - 1.96e-18    9613.614 -  7.8e-19    9747.050 - 6.55e-37
JTTDCMut+F+G4     -4777.486    9612.972 - 8.68e-19    9615.244 - 3.45e-19    9748.681 -  2.9e-37
HIVb+I+G4         -4838.442    9698.885 - 1.92e-37    9699.221 - 2.01e-37    9750.360 - 1.25e-37
PMB+F+G4          -4778.450    9614.900 - 3.31e-19    9617.172 - 1.32e-19    9750.609 - 1.11e-37
mtMet+F+I+G4      -4775.135    9610.270 - 3.35e-18    9612.702 - 1.23e-18    9750.658 - 1.08e-37
JTT+F+G4          -4778.998    9615.995 - 1.92e-19    9618.267 - 7.62e-20    9751.704 - 6.39e-38
PMB+F+I+G4        -4776.222    9612.445 - 1.13e-18    9614.876 - 4.15e-19    9752.833 - 3.63e-38
mtMet+F+G4        -4783.430    9624.859 - 2.28e-21    9627.131 - 9.06e-22    9760.568 -  7.6e-40
HIVb+G4           -4847.221    9714.442 - 8.03e-41    9714.722 - 8.65e-41    9761.238 - 5.44e-40
Blosum62+F+G4     -4784.806    9627.612 - 5.75e-22    9629.884 - 2.29e-22    9763.321 - 1.92e-40
Blosum62+F+I+G4   -4782.259    9624.519 -  2.7e-21    9626.950 - 9.92e-22    9764.907 - 8.68e-41
FLU+I+G4          -4845.984    9713.969 - 1.02e-40    9714.305 - 1.07e-40    9765.444 - 6.64e-41
Q.mammal+I+G4     -4850.226    9722.451 - 1.46e-42    9722.788 - 1.53e-42    9773.927 - 9.55e-43
Q.bird+I+G4       -4851.171    9724.342 - 5.69e-43    9724.679 - 5.95e-43    9775.818 - 3.71e-43
FLU+G4            -4856.287    9732.574 - 9.28e-45    9732.854 - 9.99e-45    9779.370 - 6.28e-44
Q.mammal+G4       -4860.506    9741.012 - 1.36e-46    9741.293 - 1.47e-46    9787.808 - 9.24e-46
Q.bird+G4         -4863.081    9746.163 - 1.04e-47    9746.443 - 1.12e-47    9792.959 - 7.03e-47
FLU+F+I+G4        -4803.431    9666.863 - 1.72e-30    9669.294 - 6.33e-31    9807.251 - 5.54e-50
mtMAM+F+I+G4      -4805.644    9671.288 - 1.89e-31    9673.719 - 6.93e-32    9811.676 - 6.07e-51
HIVb+F+I+G4       -4806.965    9673.931 - 5.03e-32    9676.362 - 1.85e-32    9814.319 - 1.62e-51
mtVer+F+I+G4      -4808.228    9676.457 - 1.42e-32    9678.888 - 5.23e-33    9816.845 - 4.58e-52
Q.mammal+F+I+G4   -4808.873    9677.746 - 7.47e-33    9680.178 - 2.74e-33    9818.134 -  2.4e-52
FLU+F+G4          -4812.951    9683.903 - 3.44e-34    9686.174 - 1.37e-34    9819.611 - 1.15e-52
HIVb+F+G4         -4815.656    9689.312 -  2.3e-35    9691.583 - 9.15e-36    9825.020 - 7.68e-54
mtMAM+F+G4        -4816.650    9691.300 - 8.51e-36    9693.572 - 3.39e-36    9827.009 - 2.84e-54
Q.mammal+F+G4     -4818.621    9695.242 - 1.19e-36    9697.514 - 4.72e-37    9830.951 - 3.96e-55
mtVer+F+G4        -4821.298    9700.595 - 8.16e-38    9702.867 - 3.25e-38    9836.303 - 2.72e-56
Q.bird+F+I+G4     -4818.887    9697.773 - 3.35e-37    9700.205 - 1.23e-37    9838.161 - 1.08e-56
Q.bird+F+G4       -4830.313    9718.627 - 9.91e-42    9720.898 - 3.94e-42    9854.335 - 3.31e-60
FLAVI+I+G4        -4909.651    9841.303 - 2.28e-68    9841.639 - 2.38e-68    9892.778 - 1.48e-68
FLAVI+F+I+G4      -4846.739    9753.479 - 2.68e-49    9755.910 - 9.84e-50    9893.867 - 8.62e-69
mtZOA+I+G4        -4912.946    9847.893 - 8.44e-70    9848.230 - 8.83e-70    9899.369 -  5.5e-70
mtZOA+G4          -4920.530    9861.060 - 1.17e-72    9861.341 - 1.26e-72    9907.856 -  7.9e-72
FLAVI+G4          -4921.129    9862.258 - 6.41e-73    9862.538 -  6.9e-73    9909.054 - 4.34e-72
FLAVI+F+G4        -4859.578    9777.156 - 1.93e-54    9779.428 - 7.69e-55    9912.865 - 6.46e-73
mtART+I+G4        -4936.533    9895.066 - 4.82e-80    9895.402 - 5.04e-80    9946.541 - 3.14e-80
mtART+G4          -4943.775    9907.550 - 9.38e-83    9907.830 - 1.01e-82    9954.346 - 6.35e-82
LG                -4967.424    9952.849 - 1.37e-92    9953.078 - 1.51e-92    9994.965 - 9.59e-91
HIVw+I+G4         -4961.731    9945.463 - 5.49e-91    9945.799 - 5.74e-91    9996.938 - 3.58e-91
mtREV+I+G4        -4965.210    9952.420 - 1.69e-92    9952.756 - 1.77e-92   10003.895 -  1.1e-92
mtREV+G4          -4973.869    9967.739 - 7.98e-96    9968.019 - 8.59e-96   10014.535 -  5.4e-95
HIVw+F+I+G4       -4907.718    9875.435 - 8.82e-76    9877.867 - 3.24e-76   10015.823 - 2.84e-95
mtMet+I+G4        -4971.840    9965.680 - 2.23e-95    9966.017 - 2.34e-95   10017.156 - 1.46e-95
HIVw+G4           -4977.561    9975.122 - 1.99e-97    9975.402 - 2.14e-97   10021.918 - 1.35e-96
mtMet+G4          -4981.233    9982.467 - 5.06e-99    9982.747 - 5.45e-99   10029.263 - 3.42e-98
HIVw+F+G4         -4922.673    9903.345 - 7.67e-82    9905.617 - 3.05e-82   10039.054 - 2.56e-100
mtMAM+I+G4        -5003.477   10028.953 - 4.07e-109   10029.290 - 4.26e-109   10080.429 - 2.65e-109
mtInv+I+G4        -5006.654   10035.307 - 1.7e-110   10035.644 - 1.78e-110   10086.783 - 1.11e-110
mtMAM+G4          -5012.493   10044.986 - 1.34e-112   10045.266 - 1.45e-112   10091.782 - 9.09e-112
mtVer+I+G4        -5009.382   10040.765 - 1.11e-111   10041.102 - 1.16e-111   10092.240 - 7.23e-112
mtInv+G4          -5015.079   10050.159 - 1.01e-113   10050.439 - 1.09e-113   10096.955 - 6.84e-113
mtVer+G4          -5020.644   10061.287 - 3.88e-116   10061.567 - 4.17e-116   10108.083 - 2.62e-115

AIC, w-AIC   : Akaike information criterion scores and weights.
AICc, w-AICc : Corrected AIC scores and weights.
BIC, w-BIC   : Bayesian information criterion scores and weights.

Plus signs denote the 95% confidence sets.
Minus signs denote significant exclusion.

SUBSTITUTION PROCESS
--------------------

Model of substitution: cpREV+I+G4

State frequencies: (model)

Model of rate heterogeneity: Invar+Gamma with 4 categories
Proportion of invariable sites: 0.4626
Gamma shape alpha: 0.7873

 Category  Relative_rate  Proportion
  0         0              0.4626
  1         0.1728         0.1344
  2         0.7480         0.1344
  3         1.7740         0.1344
  4         4.7484         0.1344
Relative rates are computed as MEAN of the portion of the Gamma distribution falling in the category.

MAXIMUM LIKELIHOOD TREE
-----------------------

Log-likelihood of the tree: -4753.6149 (s.e. 129.2064)
Unconstrained log-likelihood (without tree): -3410.0651
Number of free parameters (#branches + #model parameters): 11
Akaike information criterion (AIC) score: 9529.2298
Corrected Akaike information criterion (AICc) score: 9529.5665
Bayesian information criterion (BIC) score: 9580.7054

Total tree length (sum of branch lengths): 0.8705
Sum of internal branch lengths: 0.2131 (24.4767% of tree length)

NOTE: Tree is UNROOTED although outgroup taxon 'Avrainvillea_mazei_HV02664' is drawn at root
Numbers in parentheses are  ultrafast bootstrap support (%)

+---------------------------------------------Avrainvillea_mazei_HV02664
|
|                 +----------------------Bryopsis_plumosa_WEST4718
+-----------------| (98)
|                 +-------------------------------------Derbesia_sp_WEST4838
|
|                        +---------------------------------Caulerpa_cliftonii_HV03798
+------------------------| (100)
                         |          +---------------------Chlorodesmis_fastigiata_HV03865
                         +----------| (90)
                                    +-----Flabellia_petiolata_HV01202

Tree in newick format:

(Avrainvillea_mazei_HV02664:0.1774010564,(Bryopsis_plumosa_WEST4718:0.0911761379,Derbesia_sp_WEST4838:0.1476272926)98:0.0699127956,(Caulerpa_cliftonii_HV03798:0.1301703118,(Chlorodesmis_fastigiata_HV03865:0.0865187215,Flabellia_petiolata_HV01202:0.0245104903)90:0.0442681486)100:0.0988799497);

CONSENSUS TREE
--------------

Consensus tree is constructed from 1000 bootstrap trees
Log-likelihood of consensus tree: -4753.614908
Robinson-Foulds distance between ML tree and consensus tree: 0

Branches with support >0.000000% are kept (extended consensus)
Branch lengths are optimized by maximum likelihood on original alignment
Numbers in parentheses are bootstrap supports (%)

+---------------------------------------------Avrainvillea_mazei_HV02664
|
|                 +----------------------Bryopsis_plumosa_WEST4718
+-----------------| (98)
|                 +-------------------------------------Derbesia_sp_WEST4838
|
|                        +---------------------------------Caulerpa_cliftonii_HV03798
+------------------------| (100)
                         |          +---------------------Chlorodesmis_fastigiata_HV03865
                         +----------| (90)
                                    +-----Flabellia_petiolata_HV01202


Consensus tree in newick format: 

(Avrainvillea_mazei_HV02664:0.1774144310,(Bryopsis_plumosa_WEST4718:0.0911750125,Derbesia_sp_WEST4838:0.1476344869)98:0.0699199323,(Caulerpa_cliftonii_HV03798:0.1302020010,(Chlorodesmis_fastigiata_HV03865:0.0865481861,Flabellia_petiolata_HV01202:0.0244952561)90:0.0442550027)100:0.0988875469);

TIME STAMP
----------

Date and time: Thu Mar  7 15:44:25 2024
Total CPU time used: 2.989061 seconds (0h:0m:2s)
Total wall-clock time used: 3.119521141 seconds (0h:0m:3s)

IQ-TREE multicore version 2.2.0.3 COVID-edition for Mac OS X 64-bit built Sep  5 2022
Developed by Bui Quang Minh, James Barbetti, Nguyen Lam Tung,
Olga Chernomor, Heiko Schmidt, Dominik Schrempf, Michael Woodhams, Ly Trong Nhan.

Host:    9350L-152779-M (AVX2, FMA3, 16 GB RAM)
Command: iqtree2 -s results/supermatrix/supermatrix.protein.fa -bb 1000 -m TEST -nt 1 -redo -pre results/supermatrix/supermatrix.protein
Seed:    554947 (Using SPRNG - Scalable Parallel Random Number Generator)
Time:    Thu Mar  7 15:44:17 2024
Kernel:  AVX+FMA - 1 threads (12 CPU cores detected)

HINT: Use -nt option to specify number of threads because your CPU has 12 cores!
HINT: -nt AUTO will automatically determine the best number of threads to use.

Reading alignment file results/supermatrix/supermatrix.protein.fa ... Fasta format detected
Reading fasta file: done in 0.00131798 secs using 15.71% CPU
Alignment most likely contains protein sequences
Constructing alignment: done in 0.00306392 secs using 28.13% CPU
Alignment has 6 sequences with 796 columns, 255 distinct patterns
100 parsimony-informative, 143 singleton sites, 553 constant sites
                                 Gap/Ambiguity  Composition  p-value
Analyzing sequences: done in 0.000338078 secs using 9.465% CPU
   1  Avrainvillea_mazei_HV02664         1.13%    passed     99.95%
   2  Bryopsis_plumosa_WEST4718          0.38%    passed    100.00%
   3  Caulerpa_cliftonii_HV03798         0.00%    passed    100.00%
   4  Chlorodesmis_fastigiata_HV03865    1.51%    passed    100.00%
   5  Derbesia_sp_WEST4838               0.63%    passed    100.00%
   6  Flabellia_petiolata_HV01202        2.39%    passed    100.00%
****  TOTAL                              1.01%  0 sequences failed composition chi2 test (p-value<5%; df=19)
Checking for duplicate sequences: done in 5.91278e-05 secs using 76.11% CPU


Create initial parsimony tree by phylogenetic likelihood library (PLL)... 0.009 seconds
Perform fast likelihood tree search using LG+I+G model...
Estimate model parameters (epsilon = 5.000)
Perform nearest neighbor interchange...
Optimizing NNI: done in 0.0101578 secs using 88.3% CPU
Estimate model parameters (epsilon = 1.000)
1. Initial log-likelihood: -4763.107
Optimal log-likelihood: -4763.040
Proportion of invariable sites: 0.357
Gamma shape alpha: 0.456
Parameters optimization took 1 rounds (0.015 sec)
Time for fast ML tree search: 0.078 seconds

NOTE: ModelFinder requires 2 MB RAM!
ModelFinder will test up to 224 protein models (sample size: 796) ...
 No. Model         -LnL         df  AIC          AICc         BIC
  1  LG            4967.424     9   9952.849     9953.078     9994.965
  2  LG+I          4785.119     10  9590.237     9590.517     9637.033
  3  LG+G4         4769.787     10  9559.575     9559.855     9606.371
  4  LG+I+G4       4762.923     11  9547.846     9548.183     9599.322
  7  LG+F+G4       4752.616     29  9563.233     9565.505     9698.941
  8  LG+F+I+G4     4746.272     30  9552.545     9554.976     9692.933
 11  WAG+G4        4782.001     10  9584.002     9584.283     9630.798
 12  WAG+I+G4      4778.199     11  9578.397     9578.734     9629.873
 15  WAG+F+G4      4757.613     29  9573.227     9575.498     9708.935
 16  WAG+F+I+G4    4753.892     30  9567.784     9570.216     9708.172
 19  JTT+G4        4797.364     10  9614.729     9615.009     9661.525
 20  JTT+I+G4      4791.124     11  9604.249     9604.585     9655.724
 23  JTT+F+G4      4778.998     29  9615.995     9618.267     9751.704
 24  JTT+F+I+G4    4773.051     30  9606.102     9608.534     9746.490
WARNING: Normalizing state frequencies so that sum of them equals to 1
 27  Q.pfam+G4     4779.840     10  9579.680     9579.960     9626.476
WARNING: Normalizing state frequencies so that sum of them equals to 1
 28  Q.pfam+I+G4   4772.957     11  9567.914     9568.250     9619.389
WARNING: Normalizing state frequencies so that sum of them equals to 1
 31  Q.pfam+F+G4   4758.639     29  9575.278     9577.550     9710.987
WARNING: Normalizing state frequencies so that sum of them equals to 1
 32  Q.pfam+F+I+G4 4752.279     30  9564.559     9566.990     9704.947
WARNING: Normalizing state frequencies so that sum of them equals to 1
 35  Q.bird+G4     4863.081     10  9746.163     9746.443     9792.959
WARNING: Normalizing state frequencies so that sum of them equals to 1
 36  Q.bird+I+G4   4851.171     11  9724.342     9724.679     9775.818
WARNING: Normalizing state frequencies so that sum of them equals to 1
 39  Q.bird+F+G4   4830.313     29  9718.627     9720.898     9854.335
WARNING: Normalizing state frequencies so that sum of them equals to 1
 40  Q.bird+F+I+G4 4818.887     30  9697.773     9700.205     9838.161
WARNING: Normalizing state frequencies so that sum of them equals to 1
 43  Q.mammal+G4   4860.506     10  9741.012     9741.293     9787.808
WARNING: Normalizing state frequencies so that sum of them equals to 1
 44  Q.mammal+I+G4 4850.226     11  9722.451     9722.788     9773.927
WARNING: Normalizing state frequencies so that sum of them equals to 1
 47  Q.mammal+F+G4 4818.621     29  9695.242     9697.514     9830.951
WARNING: Normalizing state frequencies so that sum of them equals to 1
 48  Q.mammal+F+I+G4 4808.873     30  9677.746     9680.178     9818.134
 51  Q.insect+G4   4804.135     10  9628.271     9628.551     9675.067
 52  Q.insect+I+G4 4795.237     11  9612.475     9612.811     9663.950
 55  Q.insect+F+G4 4776.671     29  9611.342     9613.614     9747.050
 56  Q.insect+F+I+G4 4768.958     30  9597.915     9600.347     9738.303
WARNING: Normalizing state frequencies so that sum of them equals to 1
 59  Q.plant+G4    4794.205     10  9608.410     9608.691     9655.206
WARNING: Normalizing state frequencies so that sum of them equals to 1
 60  Q.plant+I+G4  4784.005     11  9590.009     9590.346     9641.485
WARNING: Normalizing state frequencies so that sum of them equals to 1
 63  Q.plant+F+G4  4771.916     29  9601.831     9604.103     9737.540
WARNING: Normalizing state frequencies so that sum of them equals to 1
 64  Q.plant+F+I+G4 4762.682     30  9585.365     9587.796     9725.752
WARNING: Normalizing state frequencies so that sum of them equals to 1
 67  Q.yeast+G4    4793.400     10  9606.800     9607.080     9653.596
WARNING: Normalizing state frequencies so that sum of them equals to 1
 68  Q.yeast+I+G4  4784.243     11  9590.486     9590.823     9641.962
WARNING: Normalizing state frequencies so that sum of them equals to 1
 71  Q.yeast+F+G4  4768.290     29  9594.579     9596.851     9730.288
WARNING: Normalizing state frequencies so that sum of them equals to 1
 72  Q.yeast+F+I+G4 4760.383     30  9580.766     9583.198     9721.154
 75  JTTDCMut+G4   4795.768     10  9611.536     9611.816     9658.332
 76  JTTDCMut+I+G4 4789.487     11  9600.973     9601.310     9652.449
 79  JTTDCMut+F+G4 4777.486     29  9612.972     9615.244     9748.681
 80  JTTDCMut+F+I+G4 4771.470     30  9602.940     9605.371     9743.328
 83  DCMut+G4      4815.774     10  9651.549     9651.829     9698.345
 84  DCMut+I+G4    4810.333     11  9642.665     9643.002     9694.141
 87  DCMut+F+G4    4775.776     29  9609.552     9611.823     9745.260
 88  DCMut+F+I+G4  4769.947     30  9599.894     9602.325     9740.282
 91  VT+G4         4783.028     10  9586.055     9586.335     9632.851
 92  VT+I+G4       4779.908     11  9581.816     9582.153     9633.291
 95  VT+F+G4       4771.629     29  9601.259     9603.530     9736.967
 96  VT+F+I+G4     4768.882     30  9597.764     9600.196     9738.152
 99  PMB+G4        4804.054     10  9628.108     9628.388     9674.904
100  PMB+I+G4      4801.637     11  9625.275     9625.612     9676.750
103  PMB+F+G4      4778.450     29  9614.900     9617.172     9750.609
104  PMB+F+I+G4    4776.222     30  9612.445     9614.876     9752.833
107  Blosum62+G4   4799.131     10  9618.261     9618.542     9665.057
108  Blosum62+I+G4 4796.305     11  9614.610     9614.947     9666.086
111  Blosum62+F+G4 4784.806     29  9627.612     9629.884     9763.321
112  Blosum62+F+I+G4 4782.259     30  9624.519     9626.950     9764.907
115  Dayhoff+G4    4815.703     10  9651.406     9651.687     9698.202
116  Dayhoff+I+G4  4810.213     11  9642.426     9642.763     9693.902
119  Dayhoff+F+G4  4775.697     29  9609.395     9611.666     9745.103
120  Dayhoff+F+I+G4 4769.817     30  9599.634     9602.066     9740.022
123  mtREV+G4      4973.869     10  9967.739     9968.019     10014.535
124  mtREV+I+G4    4965.210     11  9952.420     9952.756     10003.895
127  mtREV+F+G4    4770.353     29  9598.705     9600.977     9734.414
128  mtREV+F+I+G4  4763.806     30  9587.612     9590.043     9728.000
131  mtART+G4      4943.775     10  9907.550     9907.830     9954.346
132  mtART+I+G4    4936.533     11  9895.066     9895.402     9946.541
135  mtART+F+G4    4752.459     29  9562.918     9565.189     9698.626
136  mtART+F+I+G4  4744.357     30  9548.714     9551.145     9689.102
139  mtZOA+G4      4920.530     10  9861.060     9861.341     9907.856
140  mtZOA+I+G4    4912.946     11  9847.893     9848.230     9899.369
143  mtZOA+F+G4    4754.209     29  9566.418     9568.689     9702.126
144  mtZOA+F+I+G4  4745.935     30  9551.870     9554.301     9692.258
147  mtMet+G4      4981.233     10  9982.467     9982.747     10029.263
148  mtMet+I+G4    4971.840     11  9965.680     9966.017     10017.156
151  mtMet+F+G4    4783.430     29  9624.859     9627.131     9760.568
152  mtMet+F+I+G4  4775.135     30  9610.270     9612.702     9750.658
155  mtVer+G4      5020.644     10  10061.287    10061.567    10108.083
156  mtVer+I+G4    5009.382     11  10040.765    10041.102    10092.240
159  mtVer+F+G4    4821.298     29  9700.595     9702.867     9836.303
160  mtVer+F+I+G4  4808.228     30  9676.457     9678.888     9816.845
163  mtInv+G4      5015.079     10  10050.159    10050.439    10096.955
164  mtInv+I+G4    5006.654     11  10035.307    10035.644    10086.783
167  mtInv+F+G4    4760.247     29  9578.495     9580.766     9714.203
168  mtInv+F+I+G4  4753.769     30  9567.537     9569.969     9707.925
171  mtMAM+G4      5012.493     10  10044.986    10045.266    10091.782
172  mtMAM+I+G4    5003.477     11  10028.953    10029.290    10080.429
175  mtMAM+F+G4    4816.650     29  9691.300     9693.572     9827.009
176  mtMAM+F+I+G4  4805.644     30  9671.288     9673.719     9811.676
WARNING: Normalizing state frequencies so that sum of them equals to 1
179  FLAVI+G4      4921.129     10  9862.258     9862.538     9909.054
WARNING: Normalizing state frequencies so that sum of them equals to 1
180  FLAVI+I+G4    4909.651     11  9841.303     9841.639     9892.778
WARNING: Normalizing state frequencies so that sum of them equals to 1
183  FLAVI+F+G4    4859.578     29  9777.156     9779.428     9912.865
WARNING: Normalizing state frequencies so that sum of them equals to 1
184  FLAVI+F+I+G4  4846.739     30  9753.479     9755.910     9893.867
187  HIVb+G4       4847.221     10  9714.442     9714.722     9761.238
188  HIVb+I+G4     4838.442     11  9698.885     9699.221     9750.360
191  HIVb+F+G4     4815.656     29  9689.312     9691.583     9825.020
192  HIVb+F+I+G4   4806.965     30  9673.931     9676.362     9814.319
195  HIVw+G4       4977.561     10  9975.122     9975.402     10021.918
196  HIVw+I+G4     4961.731     11  9945.463     9945.799     9996.938
199  HIVw+F+G4     4922.673     29  9903.345     9905.617     10039.054
200  HIVw+F+I+G4   4907.718     30  9875.435     9877.867     10015.823
203  FLU+G4        4856.287     10  9732.574     9732.854     9779.370
204  FLU+I+G4      4845.984     11  9713.969     9714.305     9765.444
207  FLU+F+G4      4812.951     29  9683.903     9686.174     9819.611
208  FLU+F+I+G4    4803.431     30  9666.863     9669.294     9807.251
211  rtREV+G4      4791.371     10  9602.742     9603.022     9649.538
212  rtREV+I+G4    4785.918     11  9593.835     9594.172     9645.311
215  rtREV+F+G4    4761.112     29  9580.224     9582.496     9715.933
216  rtREV+F+I+G4  4755.824     30  9571.649     9574.080     9712.037
219  cpREV+G4      4757.639     10  9535.278     9535.558     9582.074
220  cpREV+I+G4    4754.045     11  9530.090     9530.427     9581.566
223  cpREV+F+G4    4741.076     29  9540.152     9542.424     9675.861
224  cpREV+F+I+G4  4737.617     30  9535.233     9537.665     9675.621
Akaike Information Criterion:           cpREV+I+G4
Corrected Akaike Information Criterion: cpREV+I+G4
Bayesian Information Criterion:         cpREV+I+G4
Best-fit model: cpREV+I+G4 chosen according to BIC

All model information printed to results/supermatrix/supermatrix.protein.model.gz
CPU time for ModelFinder: 3.481 seconds (0h:0m:3s)
Wall-clock time for ModelFinder: 4.482 seconds (0h:0m:4s)
Generating 1000 samples for ultrafast bootstrap (seed: 554947)...

NOTE: 2 MB RAM (0 GB) is required!
Estimate model parameters (epsilon = 0.100)
Thoroughly optimizing +I+G parameters from 10 start values...
Init pinv, alpha: 0.000, 0.547 / Estimate: 0.000, 0.237 / LogL: -4757.639
Init pinv, alpha: 0.077, 0.547 / Estimate: 0.080, 0.273 / LogL: -4756.785
Init pinv, alpha: 0.154, 0.547 / Estimate: 0.158, 0.320 / LogL: -4755.996
Init pinv, alpha: 0.232, 0.547 / Estimate: 0.236, 0.381 / LogL: -4755.220
Init pinv, alpha: 0.309, 0.547 / Estimate: 0.314, 0.465 / LogL: -4754.490
Init pinv, alpha: 0.386, 0.547 / Estimate: 0.390, 0.589 / LogL: -4753.890
Init pinv, alpha: 0.463, 0.547 / Estimate: 0.463, 0.787 / LogL: -4753.615
Init pinv, alpha: 0.540, 0.547 / Estimate: 0.524, 1.098 / LogL: -4753.966
Init pinv, alpha: 0.618, 0.547 / Estimate: 0.531, 1.143 / LogL: -4754.058
Init pinv, alpha: 0.695, 0.547 / Estimate: 0.528, 1.124 / LogL: -4754.018
Optimal pinv,alpha: 0.463, 0.787 / LogL: -4753.615

Parameters optimization took 0.648 sec
Wrote distance file to... 
Computing ML distances based on estimated model parameters...
Calculating distance matrix: done in 0.00639391 secs using 95.83% CPU
Computing ML distances took 0.006519 sec (of wall-clock time) 0.006206 sec (of CPU time)
Setting up auxiliary I and S matrices: done in 3.19481e-05 secs using 78.25% CPU
Constructing RapidNJ tree: done in 2.90871e-05 secs using 116.9% CPU
Computing RapidNJ tree took 0.000153 sec (of wall-clock time) 0.000119 sec (of CPU time)
Log-likelihood of RapidNJ tree: -4753.615
--------------------------------------------------------------------
|             INITIALIZING CANDIDATE TREE SET                      |
--------------------------------------------------------------------
Generating 99 parsimony trees... 0.041 second
Computing log-likelihood of 6 initial trees ... 0.016 seconds
Current best score: -4753.615

Do NNI search on 7 best initial trees
Optimizing NNI: done in 0.00745487 secs using 132.8% CPU
Optimizing NNI: done in 0.0164449 secs using 178.5% CPU
Optimizing NNI: done in 0.0193489 secs using 155.1% CPU
Optimizing NNI: done in 0.0187991 secs using 145.1% CPU
Optimizing NNI: done in 0.018518 secs using 181.5% CPU
Optimizing NNI: done in 0.020931 secs using 133.7% CPU
Optimizing NNI: done in 0.019814 secs using 169.8% CPU
Finish initializing candidate tree set (7)
Current best tree score: -4753.615 / CPU time: 0.194
Number of iterations: 7
--------------------------------------------------------------------
|               OPTIMIZING CANDIDATE TREE SET                      |
--------------------------------------------------------------------
Optimizing NNI: done in 0.0283079 secs using 97.11% CPU
Optimizing NNI: done in 0.0169098 secs using 98.26% CPU
UPDATE BEST LOG-LIKELIHOOD: -4753.615
Optimizing NNI: done in 0.0206339 secs using 95.74% CPU
Iteration 10 / LogL: -4753.673 / Time: 0h:0m:0s
Optimizing NNI: done in 0.0317531 secs using 95.56% CPU
Optimizing NNI: done in 0.024075 secs using 96.54% CPU
Optimizing NNI: done in 0.00922894 secs using 97.23% CPU
Optimizing NNI: done in 0.010438 secs using 94.58% CPU
Optimizing NNI: done in 0.031883 secs using 94.08% CPU
Optimizing NNI: done in 0.00898409 secs using 98.4% CPU
Optimizing NNI: done in 0.014647 secs using 97.71% CPU
UPDATE BEST LOG-LIKELIHOOD: -4753.615
Optimizing NNI: done in 0.0237601 secs using 95.33% CPU
Optimizing NNI: done in 0.00704908 secs using 97.59% CPU
Optimizing NNI: done in 0.0190179 secs using 96.17% CPU
Iteration 20 / LogL: -4753.634 / Time: 0h:0m:1s
Optimizing NNI: done in 0.019105 secs using 96.54% CPU
UPDATE BEST LOG-LIKELIHOOD: -4753.615
Optimizing NNI: done in 0.0201631 secs using 95.61% CPU
Optimizing NNI: done in 0.010771 secs using 96.99% CPU
Optimizing NNI: done in 0.0188301 secs using 95.24% CPU
Optimizing NNI: done in 0.0338972 secs using 95.66% CPU
Optimizing NNI: done in 0.0378041 secs using 91.17% CPU
Optimizing NNI: done in 0.022629 secs using 90.97% CPU
Optimizing NNI: done in 0.0139561 secs using 64.52% CPU
Optimizing NNI: done in 0.026201 secs using 90.88% CPU
Optimizing NNI: done in 0.011169 secs using 94.22% CPU
Iteration 30 / LogL: -4761.216 / Time: 0h:0m:1s (0h:0m:3s left)
Optimizing NNI: done in 0.0246692 secs using 81.68% CPU
Optimizing NNI: done in 0.024225 secs using 85.1% CPU
Optimizing NNI: done in 0.0181258 secs using 92.84% CPU
Optimizing NNI: done in 0.019711 secs using 97.22% CPU
Optimizing NNI: done in 0.0256929 secs using 97.7% CPU
Optimizing NNI: done in 0.0178001 secs using 95.58% CPU
Optimizing NNI: done in 0.0151432 secs using 95.22% CPU
Optimizing NNI: done in 0.0227592 secs using 81.16% CPU
Optimizing NNI: done in 0.00669384 secs using 89.83% CPU
Optimizing NNI: done in 0.0180569 secs using 89.41% CPU
Iteration 40 / LogL: -4753.615 / Time: 0h:0m:1s (0h:0m:2s left)
Optimizing NNI: done in 0.0181479 secs using 89.82% CPU
Optimizing NNI: done in 0.0193012 secs using 79.68% CPU
Optimizing NNI: done in 0.01914 secs using 89.29% CPU
Optimizing NNI: done in 0.022085 secs using 98.71% CPU
Optimizing NNI: done in 0.0165749 secs using 97.41% CPU
Optimizing NNI: done in 0.024653 secs using 96.9% CPU
Optimizing NNI: done in 0.0273499 secs using 97.15% CPU
Optimizing NNI: done in 0.0196211 secs using 95.64% CPU
Optimizing NNI: done in 0.037782 secs using 92.76% CPU
Optimizing NNI: done in 0.0219221 secs using 90.96% CPU
Iteration 50 / LogL: -4753.688 / Time: 0h:0m:1s (0h:0m:1s left)
Log-likelihood cutoff on original alignment: -4789.964
Optimizing NNI: done in 0.0110681 secs using 97.38% CPU
Optimizing NNI: done in 0.0272331 secs using 96.24% CPU
Optimizing NNI: done in 0.018425 secs using 98.19% CPU
Optimizing NNI: done in 0.00789309 secs using 96.21% CPU
Optimizing NNI: done in 0.0319381 secs using 98.47% CPU
Optimizing NNI: done in 0.014761 secs using 97.37% CPU
Optimizing NNI: done in 0.015517 secs using 97.45% CPU
Optimizing NNI: done in 0.0132799 secs using 90.83% CPU
Optimizing NNI: done in 0.0155299 secs using 95.27% CPU
Optimizing NNI: done in 0.0210369 secs using 93.57% CPU
Iteration 60 / LogL: -4753.627 / Time: 0h:0m:2s (0h:0m:1s left)
Optimizing NNI: done in 0.0297921 secs using 91.63% CPU
Optimizing NNI: done in 0.017657 secs using 90.88% CPU
Optimizing NNI: done in 0.0148749 secs using 98.65% CPU
Optimizing NNI: done in 0.014322 secs using 97.56% CPU
Optimizing NNI: done in 0.0148799 secs using 96.2% CPU
Optimizing NNI: done in 0.014076 secs using 98.79% CPU
Optimizing NNI: done in 0.00641298 secs using 99.11% CPU
Optimizing NNI: done in 0.022341 secs using 97.43% CPU
Optimizing NNI: done in 0.0212371 secs using 98% CPU
Optimizing NNI: done in 0.0110841 secs using 99.61% CPU
Iteration 70 / LogL: -4753.615 / Time: 0h:0m:2s (0h:0m:1s left)
Optimizing NNI: done in 0.01355 secs using 98.64% CPU
Optimizing NNI: done in 0.021333 secs using 98.2% CPU
Optimizing NNI: done in 0.0197899 secs using 98.62% CPU
Optimizing NNI: done in 0.013695 secs using 99.18% CPU
Optimizing NNI: done in 0.0205159 secs using 94.63% CPU
Optimizing NNI: done in 0.0179911 secs using 95.56% CPU
Optimizing NNI: done in 0.0250392 secs using 97.42% CPU
Optimizing NNI: done in 0.0071919 secs using 95.96% CPU
Optimizing NNI: done in 0.00532889 secs using 99.59% CPU
Optimizing NNI: done in 0.015403 secs using 97.92% CPU
Iteration 80 / LogL: -4753.647 / Time: 0h:0m:2s (0h:0m:0s left)
Optimizing NNI: done in 0.0120811 secs using 97.31% CPU
Optimizing NNI: done in 0.0129039 secs using 98.05% CPU
Optimizing NNI: done in 0.0137498 secs using 97.46% CPU
Optimizing NNI: done in 0.0156178 secs using 97.57% CPU
Optimizing NNI: done in 0.0172982 secs using 98.54% CPU
Optimizing NNI: done in 0.0124121 secs using 97.67% CPU
Optimizing NNI: done in 0.0133669 secs using 97.71% CPU
Optimizing NNI: done in 0.016067 secs using 99.07% CPU
Optimizing NNI: done in 0.013062 secs using 96.95% CPU
Optimizing NNI: done in 0.0136921 secs using 98.44% CPU
Iteration 90 / LogL: -4753.642 / Time: 0h:0m:2s (0h:0m:0s left)
Optimizing NNI: done in 0.0191119 secs using 98.83% CPU
Optimizing NNI: done in 0.0216322 secs using 97.96% CPU
Optimizing NNI: done in 0.027705 secs using 96.74% CPU
Optimizing NNI: done in 0.0163431 secs using 98.35% CPU
Optimizing NNI: done in 0.0173988 secs using 95.58% CPU
Optimizing NNI: done in 0.016027 secs using 97.88% CPU
Optimizing NNI: done in 0.0173781 secs using 95.82% CPU
Optimizing NNI: done in 0.015363 secs using 96.62% CPU
Optimizing NNI: done in 0.025974 secs using 96.58% CPU
Optimizing NNI: done in 0.0265579 secs using 96.99% CPU
Iteration 100 / LogL: -4753.632 / Time: 0h:0m:3s (0h:0m:0s left)
Log-likelihood cutoff on original alignment: -4789.812
NOTE: Bootstrap correlation coefficient of split occurrence frequencies: 1.000
Optimizing NNI: done in 0.012476 secs using 99.1% CPU
TREE SEARCH COMPLETED AFTER 101 ITERATIONS / Time: 0h:0m:3s

--------------------------------------------------------------------
|                    FINALIZING TREE SEARCH                        |
--------------------------------------------------------------------
Performs final model parameters optimization
Estimate model parameters (epsilon = 0.010)
1. Initial log-likelihood: -4753.615
Optimal log-likelihood: -4753.615
Proportion of invariable sites: 0.463
Gamma shape alpha: 0.787
Parameters optimization took 1 rounds (0.005 sec)
BEST SCORE FOUND : -4753.615
Creating bootstrap support values...
Split supports printed to NEXUS file results/supermatrix/supermatrix.protein.splits.nex
Total tree length: 0.870

Total number of iterations: 101
CPU time used for tree search: 2.366 sec (0h:0m:2s)
Wall-clock time used for tree search: 2.382 sec (0h:0m:2s)
Total CPU time used: 2.989 sec (0h:0m:2s)
Total wall-clock time used: 3.107 sec (0h:0m:3s)

Computing bootstrap consensus tree...
Reading input file results/supermatrix/supermatrix.protein.splits.nex...
6 taxa and 14 splits.
Consensus tree written to results/supermatrix/supermatrix.protein.contree
Reading input trees file results/supermatrix/supermatrix.protein.contree
Log-likelihood of consensus tree: -4753.615

Analysis results written to: 
  IQ-TREE report:                results/supermatrix/supermatrix.protein.iqtree
  Maximum-likelihood tree:       results/supermatrix/supermatrix.protein.treefile
  Likelihood distances:          results/supermatrix/supermatrix.protein.mldist

Ultrafast bootstrap approximation results written to:
  Split support values:          results/supermatrix/supermatrix.protein.splits.nex
  Consensus tree:                results/supermatrix/supermatrix.protein.contree
  Screen log file:               results/supermatrix/supermatrix.protein.log

ALISIM COMMAND
--------------
--alisim simulated_MSA -t results/supermatrix/supermatrix.protein.treefile -m "cpREV+I{0.462593}+G4{0.787278}" --length 796

Date and Time: Thu Mar  7 15:44:25 2024

@article{10.1093/molbev/mst010,
    author = "Katoh, Kazutaka and Standley, Daron M.",
    title = "{MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability}",
    journal = "Molecular Biology and Evolution",
    volume = "30",
    number = "4",
    pages = "772-780",
    year = "2013",
    month = "01",
    abstract = "{We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.}",
    issn = "0737-4038",
    doi = "10.1093/molbev/mst010",
    url = "https://doi.org/10.1093/molbev/mst010",
    eprint = "https://academic.oup.com/mbe/article-pdf/30/4/772/6420419/mst010.pdf"
}

@article{Emms2019,
    author = "Emms, David M. and Kelly, Steven",
    type = "Journal Article",
    title = "OrthoFinder: phylogenetic orthology inference for comparative genomics",
    journal = "Genome Biology",
    number = "1",
    doi = "10.1186/s13059-019-1832-y",
    volume = "20",
    pages = "238",
    url = "https://doi.org/10.1186/s13059-019-1832-y",
    year = "2019",
    abstract = "Here, we present a major advance of the OrthoFinder method. This extends OrthoFinder’s high accuracy orthogroup inference to provide phylogenetic inference of orthologs, rooted gene trees, gene duplication events, the rooted species tree, and comparative genomics statistics. Each output is benchmarked on appropriate real or simulated datasets, and where comparable methods exist, OrthoFinder is equivalent to or outperforms these methods. Furthermore, OrthoFinder is the most accurate ortholog inference method on the Quest for Orthologs benchmark test. Finally, OrthoFinder’s comprehensive phylogenetic analysis is achieved with equivalent speed and scalability to the fastest, score-based heuristic methods. OrthoFinder is available at https://github.com/davidemms/OrthoFinder.",
    isbn = "1474-760X",
    DA = "2019/11/14"
}

@article{10.1371/journal.pbio.3001007,
    author = "Steenwyk, Jacob L. and Buida, III, Thomas J. and Li, Yuanning and Shen, Xing-Xing and Rokas, Antonis",
    doi = "10.1371/journal.pbio.3001007",
    journal = "PLOS Biology",
    publisher = "Public Library of Science",
    title = "ClipKIT: A multiple sequence alignment trimming software for accurate phylogenomic inference",
    year = "2020",
    month = "12",
    volume = "18",
    url = "https://doi.org/10.1371/journal.pbio.3001007",
    pages = "1-17",
    abstract = "Highly divergent sites in multiple sequence alignments (MSAs), which can stem from erroneous inference of homology and saturation of substitutions, are thought to negatively impact phylogenetic inference. Thus, several different trimming strategies have been developed for identifying and removing these sites prior to phylogenetic inference. However, a recent study reported that doing so can worsen inference, underscoring the need for alternative alignment trimming strategies. Here, we introduce ClipKIT, an alignment trimming software that, rather than identifying and removing putatively phylogenetically uninformative sites, instead aims to identify and retain parsimony-informative sites, which are known to be phylogenetically informative. To test the efficacy of ClipKIT, we examined the accuracy and support of phylogenies inferred from 14 different alignment trimming strategies, including those implemented in ClipKIT, across nearly 140,000 alignments from a broad sampling of evolutionary histories. Phylogenies inferred from ClipKIT-trimmed alignments are accurate, robust, and time saving. Furthermore, ClipKIT consistently outperformed other trimming methods across diverse datasets, suggesting that strategies based on identifying and retaining parsimony-informative sites provide a robust framework for alignment trimming.",
    number = "12"
}

@article{Steenwyk_2021,
    author = "Steenwyk, Jacob L. and Buida, Thomas J. and Gon{\c{c}}alves, Carla and Goltz, Dayna C. and Morales, Grace and Mead, Matthew E. and LaBella, Abigail L. and Chavez, Christina M. and Schmitz, Jonathan E. and Hadjifrangiskou, Maria and Li, Yuanning and Rokas, Antonis",
    doi = "10.1101/2021.10.02.462868",
    url = "https://doi.org/10.1101\%2F2021.10.02.462868",
    year = "2021",
    month = "oct",
    journal = "biorxiv",
    publisher = "Cold Spring Harbor Laboratory",
    title = "{BioKIT}: a versatile toolkit for processing and analyzing diverse types of sequence data"
}

@article{10.1093/molbev/msx281,
    author = "Hoang, Diep Thi and Chernomor, Olga and von Haeseler, Arndt and Minh, Bui Quang and Vinh, Le Sy",
    title = "{UFBoot2: Improving the Ultrafast Bootstrap Approximation}",
    journal = "Molecular Biology and Evolution",
    volume = "35",
    number = "2",
    pages = "518-522",
    year = "2017",
    month = "10",
    abstract = "{The standard bootstrap (SBS), despite being computationally intensive, is widely used in maximum likelihood phylogenetic analyses. We recently proposed the ultrafast bootstrap approximation (UFBoot) to reduce computing time while achieving more unbiased branch supports than SBS under mild model violations. UFBoot has been steadily adopted as an efficient alternative to SBS and other bootstrap approaches. Here, we present UFBoot2, which substantially accelerates UFBoot and reduces the risk of overestimating branch supports due to polytomies or severe model violations. Additionally, UFBoot2 provides suitable bootstrap resampling strategies for phylogenomic data. UFBoot2 is 778 times (median) faster than SBS and 8.4 times (median) faster than RAxML rapid bootstrap on tested data sets. UFBoot2 is implemented in the IQ-TREE software package version 1.6 and freely available at http://www.iqtree.org.}",
    issn = "0737-4038",
    doi = "10.1093/molbev/msx281",
    url = "https://doi.org/10.1093/molbev/msx281",
    eprint = "https://academic.oup.com/mbe/article-pdf/35/2/518/24367824/msx281.pdf"
}

@article{10.1093/bioinformatics/btab096,
    author = "Steenwyk, Jacob L and Buida, Thomas J, III and Labella, Abigail L and Li, Yuanning and Shen, Xing-Xing and Rokas, Antonis",
    title = "{PhyKIT: a broadly applicable UNIX shell toolkit for processing and analyzing phylogenomic data}",
    journal = "Bioinformatics",
    volume = "37",
    number = "16",
    pages = "2325-2331",
    year = "2021",
    month = "02",
    abstract = "{Diverse disciplines in biology process and analyze multiple sequence alignments (MSAs) and phylogenetic trees to evaluate their information content, infer evolutionary events and processes and predict gene function. However, automated processing of MSAs and trees remains a challenge due to the lack of a unified toolkit. To fill this gap, we introduce PhyKIT, a toolkit for the UNIX shell environment with 30 functions that process MSAs and trees, including but not limited to estimation of mutation rate, evaluation of sequence composition biases, calculation of the degree of violation of a molecular clock and collapsing bipartitions (internal branches) with low support.To demonstrate the utility of PhyKIT, we detail three use cases: (1) summarizing information content in MSAs and phylogenetic trees for diagnosing potential biases in sequence or tree data; (2) evaluating gene–gene covariation of evolutionary rates to identify functional relationships, including novel ones, among genes and (3) identify lack of resolution events or polytomies in phylogenetic trees, which are suggestive of rapid radiation events or lack of data. We anticipate PhyKIT will be useful for processing, examining and deriving biological meaning from increasingly large phylogenomic datasets.PhyKIT is freely available on GitHub (https://github.com/JLSteenwyk/PhyKIT), PyPi (https://pypi.org/project/phykit/) and the Anaconda Cloud (https://anaconda.org/JLSteenwyk/phykit) under the MIT license with extensive documentation and user tutorials (https://jlsteenwyk.com/PhyKIT).Supplementary data are available at Bioinformatics online.}",
    issn = "1367-4803",
    doi = "10.1093/bioinformatics/btab096",
    url = "https://doi.org/10.1093/bioinformatics/btab096",
    eprint = "https://academic.oup.com/bioinformatics/article-pdf/37/16/2325/39948152/btab096.pdf"
}

@article{eaton_toytree_2020,
    author = "Eaton, Deren A. R.",
    title = "Toytree: {A} minimalist tree visualization and manipulation library for {Python}",
    volume = "11",
    doi = "10.1111/2041-210X.13313",
    journal = "Methods in Ecology and Evolution",
    year = "2020",
    pages = "187--191"
}

@article{10.1093/molbev/msaa015,
    author = "Minh, Bui Quang and Schmidt, Heiko A and Chernomor, Olga and Schrempf, Dominik and Woodhams, Michael D and von Haeseler, Arndt and Lanfear, Robert",
    title = "{IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era}",
    journal = "Molecular Biology and Evolution",
    volume = "37",
    number = "5",
    pages = "1530-1534",
    year = "2020",
    month = "02",
    abstract = "{IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.}",
    issn = "0737-4038",
    doi = "10.1093/molbev/msaa015",
    url = "https://doi.org/10.1093/molbev/msaa015",
    eprint = "https://academic.oup.com/mbe/article-pdf/37/5/1530/33386032/msaa015.pdf"
}

@article{Kalyaanamoorthy2017,
    author = "Kalyaanamoorthy, Subha and Minh, Bui Quang and Wong, Thomas K F and von Haeseler, Arndt and Jermiin, Lars S",
    type = "Journal Article",
    title = "ModelFinder: fast model selection for accurate phylogenetic estimates",
    journal = "Nature Methods",
    number = "6",
    doi = "10.1038/nmeth.4285",
    volume = "14",
    pages = "587--589",
    url = "https://doi.org/10.1038/nmeth.4285",
    year = "2017",
    abstract = "ModelFinder is a fast model-selection method that greatly improves the accuracy of phylogenetic estimates.",
    issn = "1548-7105",
    DA = "2017/06/01"
}

@article{Zhang2018,
    author = "Zhang, Chao and Rabiee, Maryam and Sayyari, Erfan and Mirarab, Siavash",
    type = "Journal Article",
    title = "ASTRAL-III: polynomial time species tree reconstruction from partially resolved gene trees",
    journal = "BMC Bioinformatics",
    number = "6",
    doi = "10.1186/s12859-018-2129-y",
    volume = "19",
    pages = "153",
    url = "https://doi.org/10.1186/s12859-018-2129-y",
    year = "2018",
    abstract = "Evolutionary histories can be discordant across the genome, and such discordances need to be considered in reconstructing the species phylogeny. ASTRAL is one of the leading methods for inferring species trees from gene trees while accounting for gene tree discordance. ASTRAL uses dynamic programming to search for the tree that shares the maximum number of quartet topologies with input gene trees, restricting itself to a predefined set of bipartitions.",
    issn = "1471-2105",
    DA = "2018/05/08"
}